Deep reinforcement learning to uncover autonomous navigation strategies in turbulent flows
Navigation is about optimising a route to go from A to B. Animal and robotic navigation however fundamentally differ from the routing of planes or ships, because it is autonomous : the self-propelled "agent" has only access to information from its own sensors to make decisions. This type of problem is well-suited for reinforcement learning, a branch of artificial intelligence that has gained popularity by beating human players at games. In this talk I will show how we can leverage modern (deep) reinforcement learning techniques to solve two navigation problems inspired by the animal world : the vertical migration of plankton through the water column and the search for an odor source by insects.
Contact Nathanaël Machicoane for more information or to schedule a discussion with the seminar speaker.